Learning to Avoid Collisions

Members of a multi-robot team, operating within close quarters, need to avoid crashing into each other. Simple collision avoidance methods can be used to prevent such collisions, typically by computing the distance to other robots and stopping, perhaps moving away, when this distance falls below a certain threshold. While this approach may avoid disaster, it may also reduce the team's efficiency if robots halt for a long time to let others pass by or if they travel further to move around one another. This paper reports on experiments where a human operator, through a graphical user interface, watches robots perform an exploration task. The operator can manually suspend robots' movements before they crash into each other, and then resume their movements when their paths are clear. Experiment logs record the robots' states when they are paused and resumed. A behavior pattern for collision avoidance is learned, by classifying the states of the robots' environment when the human operator issues "wait" and "resume" commands. Preliminary results indicate that it is possible to learn a classifier which models these behavior patterns, and that different human operators consider different factors when making decisions about stopping and starting robots.